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dc.contributor.authorWang, Nan
dc.contributor.authorAravinthan, Visvakumar
dc.contributor.authorDing, Yanwu
dc.identifier.citationNan Wang; Aravinthan, V.; Yanwu Ding. 2014. Feeder-level fault detection and classification with multiple sensors: A smart grid scenario. Statistical Signal Processing (SSP), 2014 IEEE Workshop on Year: 2014 Pages: 37 - 40en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractThe smart grid initiative requires self-healing distribution systems with more accurate fault detection and classification techniques. A multi-sensor feeder-level fault detection and classification algorithm is presented in this work, based on the techniques of the support vector machine and the principal components. An IEEE 34-bus feeder model with dynamic loading conditions is used to evaluate the developed algorithm. Noise in the three-phase current measurements is applied. The numerical analysis indicates that high accuracies in fault detection and classification are achieved for the proposed algorithm.en_US
dc.publisherIEEE Conference Publicationsen_US
dc.relation.ispartofseries2014 IEEE Workshop on Statistical Signal Processing (SSP);
dc.subjectPrincipal componenten_US
dc.subjectSupport vector machineen_US
dc.subjectSmart griden_US
dc.subjectDistribution feeder faulten_US
dc.titleFeeder-level fault detection and classification with multiple sensors: a smart grid scenarioen_US
dc.typeConference paperen_US
dc.rights.holder© Copyright 2015 IEEE - All rights reserved.en_US

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